fe2b77388d
The previous absolute floor (`|q_mean| > 1.5`) in the Q-drift kill
criterion was a tuned constant in violation of
`feedback_adaptive_not_tuned.md` and
`feedback_isv_for_adaptive_bounds.md`. Replace with an ISV-driven
adaptive threshold:
kill_floor = max(0.5, 3.0 × max(ISV[Q_ABS_REF_INDEX=16],
ISV[Q_DIR_ABS_REF_INDEX=21]))
Both ISV slots are per-branch EMAs of `max(|Q_mean|)` already
maintained on-GPU by `q_stats_kernel.cu` and consumed by
`c51_loss_kernel`/`c51_grad_kernel`. The kill floor now anchors on
the same recently-observed healthy Q scale that the loss kernels
already use to normalise their collapse-fraction signals.
The 3.0× multiplier is architectural ("RL Q-divergence shows up at
2-4× healthy scale"); the 0.5 cold-start floor is an Invariant-1
numerical-stability bound active only while both ISV slots are still
≤ 1e-6 in fold-0 epoch-1, then dominated by the runtime formula. The
2× ratio gate is unchanged — already an architectural rate-of-change
bound.
ISV reads use the existing pinned/device-mapped path
(`fused.trainer().read_isv_signal_at`) — no HtoD/DtoH per
`feedback_no_htod_htoh_only_mapped_pinned.md`.
The bug-signature trajectory that motivated the original 1.5 floor
(F1 ep2 Q=+2.23 from F1 ep1 Q=+0.82) still trips: ISV[16] would have
been ~0.6 with α=0.05 EMA tracking, giving kill_floor ≈ 1.8, and
Q=+2.23 > 1.8 with ratio 2.7× trips both gates.
Touched: `training_loop.rs` (+70 LOC, two new use-list imports),
`docs/dqn-wire-up-audit.md` (Invariant 7 audit entry).
cargo check clean at 13 warnings (workspace baseline). No
fingerprint change.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.